The Self-medication Hypothesis in Schizophrenia: What Have We Learned from Animal Models?
Bibliographic record
Abstract
There is a high prevalence of substance use and substance use disorder in patients with schizophrenia, compared with control subjects. A number of theories have been proposed to explain the high prevalence of substance use among schizophrenics. The main theories are the addiction vulnerability hypothesis, the antipsychotic-induced vulnerability hypothesis and the self-medication hypothesis. In this chapter we cover the data evaluating the self-medication hypothesis using an animal model perspective. We cover tobacco and cannabis, which are the two most important drugs for this hypothesis. First, we describe the clinical aspects and the animal models of schizophrenia that have been used to test the self-medication hypothesis. The animal literature is then introduced. From these studies, it appears that there is some support for the addiction vulnerability hypothesis for nicotine, but there is limited support for the self-medication hypothesis with nicotine. For cannabinoid agonists, there are no data covering the addiction vulnerability hypothesis. There is a clear detrimental effect of cannabinoid agonists on cognition, but, surprisingly, some studies suggest that cannabinoid agonists may improve some measures of cognition in models of schizophrenia. All those interpretations should be considered to be preliminary, due to the limited work that has been conducted so far testing these hypotheses directly. However, this does present novel strategies to correct the cognitive dysfunction associated with schizophrenia, and these warrant further exploration using both preclinical and clinical approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".